10. Choropleth Map

Choroplet Map

import folium 
import pandas as pd 
import json ## json python 상에서 쓸 수 있게하는 패키지
import requests  ## 인터넷상의 자료를 긁어오는 패키지

- 아래 같은지도에 Choropleth Map 처럼 선을 그린다.

- github에 선들을 그려주는 위도, 경도 데이터가 저장되어 있음

m = folium.Map([36,128],zoom_start=7,scrollWheelZoom=False)
m

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순서 1 : 지역구 그리기

- 링크 저장

local_distriction_jsonurl='https://raw.githubusercontent.com/southkorea/southkorea-maps/master/kostat/2018/json/skorea-municipalities-2018-geo.json'
global_distriction_jsonurl='https://raw.githubusercontent.com/southkorea/southkorea-maps/master/kostat/2018/json/skorea-provinces-2018-geo.json'

- json 파일 load \(\to\) 복잡한 형채의 dic 형태로 저장되어 있음

local_dict = json.loads(requests.get(local_distriction_jsonurl).text) ## 시군구별 json 파일
global_dict = json.loads(requests.get(global_distriction_jsonurl).text) ## 도별 json 파일

#global_dict

global scale로 시각화

m = folium.Map(
                location = [36,128],
                zoom_start =7,
                scrollWheelZoom = False
                )
folium.Choropleth(geo_data=global_dict).add_to(m)
m

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json파일 뜯어보기

- local_dict를 살펴보자.

local_dict?

Type:        dict
String form: {'type': 'FeatureCollection', 'features': [{'type': 'Feature', 'geometry': {'type': 'MultiPolygon <...>  'name': 'sgg', 'crs': {'type': 'name', 'properties': {'name': 'urn:ogc:def:crs:OGC:1.3:CRS84'}}}
Length:      4
Docstring:  
dict() -> new empty dictionary
dict(mapping) -> new dictionary initialized from a mapping object's
    (key, value) pairs
dict(iterable) -> new dictionary initialized as if via:
    d = {}
    for k, v in iterable:
        d[k] = v
dict(**kwargs) -> new dictionary initialized with the name=value pairs
    in the keyword argument list.  For example:  dict(one=1, two=2)

- 아래와 같이 4개의 원소를 가지는 딕셔너리 임을 확인

local_dict.keys()

dict_keys(['type', 'features', 'name', 'crs'])

local_dict["type"]

'FeatureCollection'

local_dict['name']

'sgg'

local_dict['crs']

{'type': 'name', 'properties': {'name': 'urn:ogc:def:crs:OGC:1.3:CRS84'}}

- 위 3개는 별거 없어보임

• features 길이가 250인 리스트형태이다.
• 리스트내의 요소들은각 지역의 위도 경도 데이터를 가지고 있는것 같다.

_features =local_dict["features"]

_features[0]

{'type': 'Feature',
 'geometry': {'type': 'MultiPolygon',
  'coordinates': [[[[126.97468086053324, 37.629805433945975],
     [126.97496550641705, 37.62902584732434],
     [126.97619200024447, 37.629086831818235],
     [126.97841861289852, 37.62929009924553],
     [126.97952791872238, 37.62889299772442],
     [126.97993254143336, 37.62855138674378],
     [126.97997245663913, 37.62845560448294],
     [126.98004544991201, 37.628280435760445],
     [126.97960294035302, 37.626371002121644],
     [126.98090237068868, 37.625391798020395],
     [126.98168803207568, 37.62449661776251],
     [126.98184774907679, 37.62431463061319],
     [126.98310978892584, 37.622104640078],
     [126.98365101056845, 37.62115092912986],
     [126.98355888565348, 37.620963267437894],
     [126.98317999257362, 37.62018285183479],
     [126.9831620682276, 37.6192347829173],
     [126.9837143460292, 37.61775856638039],
     [126.98453643307462, 37.61598061074333],
     [126.985901535693, 37.612194740823796],
     [126.9857804843237, 37.61090698776889],
     [126.98586523076781, 37.60946995153532],
     [126.98623831468637, 37.607736782107125],
     [126.98675598705915, 37.60608704965549],
     [126.98677074181907, 37.60458661863664],
     [126.9866691435352, 37.604023824468506],
     [126.98589778740545, 37.60363176153766],
     [126.98259603020061, 37.60224271097029],
     [126.9806843648502, 37.60161031225276],
     [126.97880665420949, 37.60101042316433],
     [126.97852169405631, 37.60099248777431],
     [126.97841693568347, 37.600914296754326],
     [126.97830701720335, 37.600830447643865],
     [126.97768193317589, 37.60024002437466],
     [126.9774909383084, 37.599649166074286],
     [126.97719804274786, 37.59864847380904],
     [126.97672044958631, 37.59790077442683],
     [126.97713557023776, 37.59768356016227],
     [126.97874017601505, 37.59663493476611],
     [126.97911091252222, 37.596394270916285],
     [126.98148823597326, 37.59533576215007],
     [126.98292059990969, 37.594775982193624],
     [126.98384405286397, 37.59443543461552],
     [126.98432317109803, 37.59437806733553],
     [126.98450416009975, 37.59437471448865],
     [126.9846156223434, 37.59438909275369],
     [126.98463785016311, 37.59439408049888],
     [126.98464216134765, 37.59439557572296],
     [126.98466445483236, 37.59440401859687],
     [126.98474441343951, 37.594434717703415],
     [126.98479058424006, 37.594451906863895],
     [126.98487868712496, 37.59446656147479],
     [126.98514831673403, 37.594501790677405],
     [126.98542946037894, 37.59444945443447],
     [126.98577892249222, 37.59425495254234],
     [126.98851705023388, 37.59194143989685],
     [126.98886704210804, 37.59164472228141],
     [126.98907495209343, 37.59142090222344],
     [126.98908433025534, 37.59140908294205],
     [126.98962720754243, 37.59139763837295],
     [126.99366158210871, 37.59149362412968],
     [126.99744340490047, 37.59221011675905],
     [126.99845395445548, 37.59247126210828],
     [127.00182654761254, 37.59117745103071],
     [127.00408062474574, 37.58773357887768],
     [127.00656012457411, 37.58553460128624],
     [127.00863797169242, 37.58045993171883],
     [127.01051131122689, 37.58025727035416],
     [127.0111260694579, 37.58104392936677],
     [127.0111791729006, 37.58109176738164],
     [127.01169858419944, 37.58150458110694],
     [127.0117885968101, 37.58157458058202],
     [127.0120845128743, 37.581585674737156],
     [127.01222457400928, 37.581550873062085],
     [127.0124187027769, 37.58150235525185],
     [127.01253278245557, 37.58147102693477],
     [127.01262598550882, 37.58144402349896],
     [127.01274334585422, 37.58141185855377],
     [127.01285982629548, 37.58138609392204],
     [127.01314825156626, 37.58143362114355],
     [127.01324571819538, 37.58145032393419],
     [127.0141191759168, 37.581820171723166],
     [127.01474563580459, 37.582305831258346],
     [127.01483334205895, 37.58233145649853],
     [127.01491713941482, 37.582244907556685],
     [127.01638580146992, 37.58193775580977],
     [127.01673363020068, 37.58169269831286],
     [127.01829142498933, 37.579049416348774],
     [127.01830026223921, 37.57903270179077],
     [127.0187770633223, 37.57751723332606],
     [127.02179265953254, 37.57888146291368],
     [127.02313849621643, 37.57802087910892],
     [127.02336168058478, 37.57191110395814],
     [127.0231368263208, 37.57196081106164],
     [127.01902025943656, 37.57101323682973],
     [127.01733386994712, 37.570076865408346],
     [127.01003508740953, 37.569770822171066],
     [127.00983517715241, 37.569764939352325],
     [127.00667501959796, 37.5696322906642],
     [127.00331554021028, 37.56962809025019],
     [127.00166446511412, 37.56954949223042],
     [126.99997987347145, 37.5692153342546],
     [126.99979663625149, 37.569179012484],
     [126.99919901238287, 37.569061584986855],
     [126.99894910731591, 37.56901456760983],
     [126.99872661605082, 37.56897317063658],
     [126.99854409537699, 37.568939937981746],
     [126.99823033791377, 37.56888559029638],
     [126.99795832632955, 37.56884081225306],
     [126.99734249535118, 37.56876646165091],
     [126.99683684418163, 37.568709851806034],
     [126.99646755692298, 37.56866901280384],
     [126.99624859929914, 37.56864873037851],
     [126.9908841263958, 37.568202068530944],
     [126.99015096670833, 37.56813614100136],
     [126.9899958620981, 37.568128802881496],
     [126.98961154520899, 37.5681113107368],
     [126.98956785452701, 37.56810990322031],
     [126.98946917417102, 37.5681067923079],
     [126.98894530081218, 37.56810280048161],
     [126.98892726902085, 37.56810279903639],
     [126.98892019626068, 37.56810279906341],
     [126.98838782923946, 37.56812610223998],
     [126.9882334327857, 37.568133130541256],
     [126.98821751956565, 37.56813396831202],
     [126.98663917909906, 37.568232328822624],
     [126.97973830842082, 37.5690421486377],
     [126.97762523789515, 37.569298907580105],
     [126.97566360541524, 37.56938088043951],
     [126.96909869121171, 37.56824768293371],
     [126.96669861216496, 37.565820355825934],
     [126.9657113705709, 37.56677550324494],
     [126.95940037405296, 37.57304934989536],
     [126.95491745681615, 37.577149174890465],
     [126.95355819355073, 37.578748855084044],
     [126.95362029368313, 37.57880909633655],
     [126.95720618763337, 37.5798655310825],
     [126.9575728370125, 37.58230278085636],
     [126.95799889706633, 37.58412468840079],
     [126.95799717111329, 37.58444209574531],
     [126.95790145320846, 37.584703977736275],
     [126.95782691437041, 37.58485252016721],
     [126.95784782389684, 37.585019116837834],
     [126.95783833056504, 37.58504093891924],
     [126.9578273214224, 37.58506624584095],
     [126.95747215846923, 37.5860469085176],
     [126.95737395766116, 37.587076849872744],
     [126.9573886107174, 37.58906602474426],
     [126.95749035641607, 37.59007828277017],
     [126.95779750312285, 37.592573592797144],
     [126.95852639147067, 37.59418568474202],
     [126.95902399975766, 37.59479744400614],
     [126.95903333866578, 37.59505902103998],
     [126.95826848335163, 37.597788688618316],
     [126.9580418874916, 37.598155874167304],
     [126.95771415677586, 37.59836707719559],
     [126.9573078320294, 37.5985310319704],
     [126.95654122604887, 37.598827579847494],
     [126.95634147535245, 37.59892143950143],
     [126.95414105350225, 37.59909827963582],
     [126.95338041139009, 37.599531288469684],
     [126.95259689745177, 37.60122903220133],
     [126.95250806229828, 37.60156792706982],
     [126.95237511989355, 37.602476036294],
     [126.95356668581537, 37.60447934849368],
     [126.95376271651638, 37.60469036467159],
     [126.95388420830209, 37.60480196572351],
     [126.95254683214502, 37.60665255755602],
     [126.95220372203077, 37.60688813942989],
     [126.95056465460655, 37.60835968464961],
     [126.95033780725277, 37.60909261594867],
     [126.95036280527036, 37.610622635741834],
     [126.94982875644766, 37.61165624620594],
     [126.94986821104538, 37.611730018151725],
     [126.95047793576704, 37.6128699970444],
     [126.95073164301206, 37.61352828204463],
     [126.95079658268352, 37.613849365294406],
     [126.95072414848573, 37.615537943529716],
     [126.95046953204748, 37.616099153508785],
     [126.94986337153628, 37.61872668218891],
     [126.94889796158519, 37.623262743943236],
     [126.94900026337734, 37.6243351349276],
     [126.94971290787633, 37.625344069539224],
     [126.94981195859722, 37.625497586539126],
     [126.95045372101613, 37.626418006443956],
     [126.95556524710337, 37.62828290175954],
     [126.95841841842733, 37.629469751140356],
     [126.96011573909414, 37.62977556572832],
     [126.96708433443806, 37.63070658019315],
     [126.97302977391784, 37.6323747679206],
     [126.97538835725105, 37.63173308759023],
     [126.97536907543345, 37.631698170595676],
     [126.97508476623943, 37.63118349920217],
     [126.97468086053324, 37.629805433945975]]]]},
 'properties': {'name': '종로구',
  'base_year': '2018',
  'name_eng': 'Jongno-gu',
  'code': '11010'}}

• features 의 내부구조를 또 살펴보니
• geometry는 위도 경도, type은 별거 없고, properties 의 경우 이름, 연도, 영문 이름, 시도코드가 저장되어있다.

_features[0].keys()

dict_keys(['type', 'geometry', 'properties'])

_features[0]["properties"]

{'name': '종로구', 'base_year': '2018', 'name_eng': 'Jongno-gu', 'code': '11010'}

연습 : 덕진구와 완산구를 찾아보자.

- 지역 이름을 가지는 리스트를 생성

_lst = [_features[i]["properties"]["name"] for i in range(250)]

_lst[:10]

['종로구', '중구', '용산구', '성동구', '광진구', '동대문구', '중랑구', '성북구', '강북구', '도봉구']

- index 함수 이용

_lst.index("전주시덕진구")

166

_lst.index("전주시완산구")

165

_keywords = ["전주시덕진구","전주시완산구"]
_jj = [_lst.index(keyword) for keyword in _keywords]
_jj

[166, 165]

- view vs copy

• 딕셔너리의 경우 copy를 명시하지 않으면 두 개의 객체가 동시의 변경된다.

• 따라서 딕셔너리를 이용할 경우 카피를 명시하고 사용하자

시각화

df = pd.DataFrame({'key':['전주시덕진구','전주시완산구'],'value':[20,30]})
df

	key	value
0	전주시덕진구	20
1	전주시완산구	30

local_dict2 = local_dict.copy()

local_dict2["features"] = [local_dict["features"][j] for j in _jj]

m = folium.Map([35.84195368311022, 127.1155556693179],zoom_start=11,scrollWheelZoom=False)
choro = folium.Choropleth(
    data=df, 
    geo_data=local_dict2,
    columns=['key','value'],
    key_on = 'feature.properties.name'
)
choro.add_to(m) 
m

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요약

- folium.Choropleth의 사용법

• 2개의 데이터를 연결해야함 : 하나는 일반 데이터 프레임, 하나는 json에서 나온 dict
• 데이터 연결을 위해서는 공유가능한 연결의 매체가 필요하다.
• 연결의 매개체는 df-key, local_dct2[”],local_dict2['features'][?]['properties']['name']을 이용했다.
• 연결의 매개체를 name 이아닌 다른 것들도 이용할 수 있다.

m = folium.Map([35.84195368311022, 127.1155556693179],zoom_start=11,scrollWheelZoom=False)

choro = folium.Choropleth(
    data=df,  ## data1 
    geo_data=local_dict2, ## data2 (이 시점에서 폴리곤에 대한 정보가 choro 인스턴스에 전달) 
    columns=['key','value'], ## data1에서 중요한것들을 나열. 항상 [key,value]의 조합으로 써야한다. 
        # 이때 ['key','value']는 [data2와의 매개체,지도에서 색의 단계를 표현하는 변수]로 해석가능
    key_on = 'feature.properties.name' ## data2에서 중요한것: 즉 data1과의 연결매개체 
)
choro.add_to(m)
m

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대한민국 인구수 (global scale)

df=pd.read_csv('https://raw.githubusercontent.com/guebin/2021DV/master/_notebooks/2021-11-22-prov.csv')
df

	행정구역(시군구)별	총인구수 (명)
0	서울특별시	9532428
1	부산광역시	3356311
2	대구광역시	2390721
3	인천광역시	2945009
4	광주광역시	1442454
5	대전광역시	1454228
6	울산광역시	1122566
7	세종특별자치시	368276
8	경기도	13549577
9	강원도	1537717
10	충청북도	1596948
11	충청남도	2118977
12	전라북도	1789770
13	전라남도	1834653
14	경상북도	2627925
15	경상남도	3318161
16	제주특별자치도	676569

m = folium.Map(
    location = [36,128],
    zoom_start=7,
    scrollWheelZoom = False
)
folium.Choropleth(
    geo_data=global_dict,
    data=df,
    columns=['행정구역(시군구)별','총인구수 (명)'],
    key_on='properties.name',
).add_to(m)
m

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대한민국 인구수 (local scale)

df=pd.read_csv('https://raw.githubusercontent.com/guebin/2021DV/master/_notebooks/2021-11-22-muni.csv')
df

	행정구역(시군구)별	총인구수 (명)
0	종로구	145346
1	중구	122781
2	용산구	223713
3	성동구	287174
4	광진구	340814
...	...	...
269	함양군	38475
270	거창군	61242
271	합천군	43029
272	제주시	493225
273	서귀포시	183344

274 rows × 2 columns

m = folium.Map([35.84195368311022, 127.1155556693179],zoom_start=7,scrollWheelZoom=False)
choro= folium.Choropleth(
    data=df, 
    geo_data= local_dict, 
    columns=['행정구역(시군구)별','총인구수 (명)'], 
    key_on='feature.properties.name'
)
choro.add_to(m) 
m

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Choropleth Map of Plotly

import plotly.express as px
from IPython.display import HTML

df = px.data.election()
geojson = px.data.election_geojson()

fig = px.choropleth_mapbox(df, ## 데이터 1 
                           geojson=geojson, ## 데이터 2
                           color="Bergeron",
                           locations="district", featureidkey="properties.district",
                           center={"lat": 45.5517, "lon": -73.7073},
                           mapbox_style="carto-positron", zoom=9)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
HTML(fig.to_html(include_plotlyjs='cdn',include_mathjax=False, config=dict({'scrollZoom':False})))
fig.show()

df.head()

	district	Coderre	Bergeron	Joly	total	winner	result	district_id
0	101-Bois-de-Liesse	2481	1829	3024	7334	Joly	plurality	101
1	102-Cap-Saint-Jacques	2525	1163	2675	6363	Joly	plurality	102
2	11-Sault-au-Récollet	3348	2770	2532	8650	Coderre	plurality	11
3	111-Mile-End	1734	4782	2514	9030	Bergeron	majority	111
4	112-DeLorimier	1770	5933	3044	10747	Bergeron	majority	112

• data2 즉 위치 정보 데이터는 properties의 district 혹은 id를 key로 한다.

geojson['features']

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함수 입력구조

df = px.data.election() ### 일반적인 데이터 프레임 
geojson = px.data.election_geojson() ### json파일 

fig = px.choropleth_mapbox(df,  ### 데이터프레임 
                           geojson=geojson, ### json파일 
                           color="Bergeron", ### df에서 코로플레스의 단계를 표시 
                           locations="district", ### df에 존재하는 연결변수 
                           featureidkey="properties.district", ### json에 존재하는 연결매개체
                           center={"lat": 45.5517, "lon": -73.7073}, 
                           mapbox_style="carto-positron", 
                           zoom=9)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show(config=dict({'scrollZoom':False}))

한국의 인구수를 plotly로 시각화

df=pd.read_csv('https://raw.githubusercontent.com/guebin/2021DV/master/_notebooks/2021-11-22-prov.csv')
df

	행정구역(시군구)별	총인구수 (명)
0	서울특별시	9532428
1	부산광역시	3356311
2	대구광역시	2390721
3	인천광역시	2945009
4	광주광역시	1442454
5	대전광역시	1454228
6	울산광역시	1122566
7	세종특별자치시	368276
8	경기도	13549577
9	강원도	1537717
10	충청북도	1596948
11	충청남도	2118977
12	전라북도	1789770
13	전라남도	1834653
14	경상북도	2627925
15	경상남도	3318161
16	제주특별자치도	676569

global_dict["features"][0]["properties"]

{'name': '서울특별시', 'base_year': '2018', 'name_eng': 'Seoul', 'code': '11'}

geojson = global_dict ### json파일 

fig = px.choropleth_mapbox(df,  ### 데이터프레임 
                           geojson=geojson, ### json파일 
                           color="총인구수 (명)", ### df에서 코로플레스의 단계를 표시 
                           locations="행정구역(시군구)별", ### df에 존재하는 연결변수 
                           featureidkey="properties.name", ### json에 존재하는 연결매개체
                           center={"lat": 35.84195368311022, "lon": 127.1155556693179}, ## 전주를 center로 잡아줌
                           mapbox_style="carto-positron", 
                           zoom=6)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show(config=dict({'scrollZoom':True}))

판다스 백엔드

import numpy as np 
import pandas as pd 
import warnings
import matplotlib.pyplot as plt
%matplotlib inline

- 핸드폰 점유율

df = pd.read_csv('https://raw.githubusercontent.com/kalilurrahman/datasets/main/mobilephonemktshare2020.csv')
df

	Date	Samsung	Apple	Huawei	Xiaomi	Oppo	Mobicel	Motorola	LG	Others	Realme	Google	Nokia	Lenovo	OnePlus	Sony	Asus
0	2019-10	31.49	22.09	10.02	7.79	4.10	3.15	2.41	2.40	9.51	0.54	2.35	0.95	0.96	0.70	0.84	0.74
1	2019-11	31.36	22.90	10.18	8.16	4.42	3.41	2.40	2.40	9.10	0.78	0.66	0.97	0.97	0.73	0.83	0.75
2	2019-12	31.37	24.79	9.95	7.73	4.23	3.19	2.50	2.54	8.13	0.84	0.75	0.90	0.87	0.74	0.77	0.70
3	2020-01	31.29	24.76	10.61	8.10	4.25	3.02	2.42	2.40	7.55	0.88	0.69	0.88	0.86	0.79	0.80	0.69
4	2020-02	30.91	25.89	10.98	7.80	4.31	2.89	2.36	2.34	7.06	0.89	0.70	0.81	0.77	0.78	0.80	0.69
5	2020-03	30.80	27.03	10.70	7.70	4.30	2.87	2.35	2.28	6.63	0.93	0.73	0.72	0.74	0.78	0.76	0.66
6	2020-04	30.41	28.79	10.28	7.60	4.20	2.75	2.51	2.28	5.84	0.90	0.75	0.69	0.71	0.80	0.76	0.70
7	2020-05	30.18	26.72	10.39	8.36	4.70	3.12	2.46	2.19	6.31	1.04	0.70	0.73	0.77	0.81	0.78	0.76
8	2020-06	31.06	25.26	10.69	8.55	4.65	3.18	2.57	2.11	6.39	1.04	0.68	0.74	0.75	0.77	0.78	0.75
9	2020-07	30.95	24.82	10.75	8.94	4.69	3.46	2.45	2.03	6.41	1.13	0.65	0.76	0.74	0.76	0.75	0.72
10	2020-08	31.04	25.15	10.73	8.90	4.69	3.38	2.39	1.96	6.31	1.18	0.63	0.74	0.72	0.75	0.73	0.70
11	2020-09	30.57	24.98	10.58	9.49	4.94	3.50	2.27	1.88	6.12	1.45	0.63	0.74	0.67	0.81	0.69	0.67
12	2020-10	30.25	26.53	10.44	9.67	4.83	2.54	2.21	1.79	6.04	1.55	0.63	0.69	0.65	0.85	0.67	0.64

matplotlib : 2개의 y를 겹처그리기

df.plot.bar(x="Date", y= ["Samsung","Apple"],figsize=(10,5))

<Axes: xlabel='Date'>

막대 width 조정

df.plot.bar(x="Date",y = ["Samsung","Apple"],figsize=(10,5),width = 0.8)

<Axes: xlabel='Date'>

barh = 그래프 가로로

df.plot.barh(x='Date',y=['Samsung','Apple'],figsize=(5,10))

<Axes: ylabel='Date'>

subplot 적용

df.plot.bar(x='Date',figsize=(15,10), subplots=True, layout=(4,4), legend=False)

array([[<Axes: title={'center': 'Samsung'}, xlabel='Date'>,
        <Axes: title={'center': 'Apple'}, xlabel='Date'>,
        <Axes: title={'center': 'Huawei'}, xlabel='Date'>,
        <Axes: title={'center': 'Xiaomi'}, xlabel='Date'>],
       [<Axes: title={'center': 'Oppo'}, xlabel='Date'>,
        <Axes: title={'center': 'Mobicel'}, xlabel='Date'>,
        <Axes: title={'center': 'Motorola'}, xlabel='Date'>,
        <Axes: title={'center': 'LG'}, xlabel='Date'>],
       [<Axes: title={'center': 'Others'}, xlabel='Date'>,
        <Axes: title={'center': 'Realme'}, xlabel='Date'>,
        <Axes: title={'center': 'Google'}, xlabel='Date'>,
        <Axes: title={'center': 'Nokia'}, xlabel='Date'>],
       [<Axes: title={'center': 'Lenovo'}, xlabel='Date'>,
        <Axes: title={'center': 'OnePlus'}, xlabel='Date'>,
        <Axes: title={'center': 'Sony'}, xlabel='Date'>,
        <Axes: title={'center': 'Asus'}, xlabel='Date'>]], dtype=object)

평균시각화

df.melt(id_vars = "Date")

	Date	variable	value
0	2019-10	Samsung	31.49
1	2019-11	Samsung	31.36
2	2019-12	Samsung	31.37
3	2020-01	Samsung	31.29
4	2020-02	Samsung	30.91
...	...	...	...
203	2020-06	Asus	0.75
204	2020-07	Asus	0.72
205	2020-08	Asus	0.70
206	2020-09	Asus	0.67
207	2020-10	Asus	0.64

208 rows × 3 columns

df.melt(id_vars = "Date").groupby("variable")\
    .agg(np.mean).reset_index().\
    plot.bar(x="variable",y="value",legend=False)

FutureWarning:

The operation <function mean at 0x7fbbd41484c0> failed on a column. If any error is raised, this will raise an exception in a future version of pandas. Drop these columns to avoid this warning.

<Axes: xlabel='variable'>

sorting 후 시각화

df.melt(id_vars = "Date").groupby("variable")\
    .agg(np.mean).reset_index().\
    sort_values("value",ascending=False).\
    plot.bar(x="variable",y="value",legend=False)

FutureWarning:

The operation <function mean at 0x7fbbd41484c0> failed on a column. If any error is raised, this will raise an exception in a future version of pandas. Drop these columns to avoid this warning.

<Axes: xlabel='variable'>

plotly

df.melt(id_vars="Date").groupby("variable").\
        agg(np.mean).reset_index().\
        sort_values("value",ascending=False).\
        plot.bar(backend="plotly",x="variable",y="value")

FutureWarning:

The operation <function mean at 0x7fbbd41484c0> failed on a column. If any error is raised, this will raise an exception in a future version of pandas. Drop these columns to avoid this warning.

회사별 색깔 입히기

df.melt(id_vars='Date').\
plot.bar(x='Date',y='value',color='variable',backend='plotly')

삼성, 애플, Huawel만 표시

df.melt(id_vars="Date").\
            query("variable == 'Samsung' | variable == 'Apple' | variable == 'Huawei'").\
             plot.bar(x="Date",y="value",color="variable",backend="plotly",barmode="group")

+ text 수치

df.melt(id_vars='Date').query("variable=='Samsung' or variable=='Apple' or variable=='Huawei'" ).\
plot.bar(x='Date',y='value',color='variable',
            backend='plotly',barmode='group',text='value')

+ fact_col

df.melt(id_vars='Date').query("variable=='Samsung' or variable=='Apple' or variable=='Huawei'" ).\
plot.bar(x='Date',y='value',color='variable',backend='plotly',facet_col='variable')

+ facet_row

df.melt(id_vars='Date').query("variable=='Samsung' or variable=='Apple' or variable=='Huawei'" ).\
plot.bar(y='Date',x='value',color='variable',backend='plotly',facet_row='variable',height=700)